Gabriele Orlando
Gabriele Orlando
Post Doc, Switch Lab, KULeuven
Verified email at
Cited by
Cited by
Critical assessment of protein intrinsic disorder prediction
M Necci, D Piovesan, SCE Tosatto
Nature methods 18 (5), 472-481, 2021
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins
D Raimondi, I Tanyalcin, J Ferté, A Gazzo, G Orlando, T Lenaerts, ...
Nucleic acids research 45 (W1), W201-W206, 2017
Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities
N Louros, G Orlando, M De Vleeschouwer, F Rousseau, J Schymkowitz
Nature communications 11 (1), 3314, 2020
Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates
G Orlando, D Raimondi, F Tabaro, F Codice, Y Moreau, WF Vranken
Bioinformatics 35 (22), 4617-4623, 2019
Exploring the sequence-based prediction of folding initiation sites in proteins
D Raimondi, G Orlando, R Pancsa, T Khan, WF Vranken
Scientific reports 7 (1), 8826, 2017
Prediction of disordered regions in proteins with recurrent neural networks and protein dynamics
G Orlando, D Raimondi, F Codice, F Tabaro, W Vranken
Journal of Molecular Biology 434 (12), 167579, 2022
Role and therapeutic potential of liquid–liquid phase separation in amyotrophic lateral sclerosis
D Pakravan, G Orlando, V Bercier, L Van Den Bosch
Journal of molecular cell biology 13 (1), 15-28, 2021
Insight into the protein solubility driving forces with neural attention
D Raimondi, G Orlando, P Fariselli, Y Moreau
PLoS computational biology 16 (4), e1007722, 2020
Accurate prediction of protein beta-aggregation with generalized statistical potentials
G Orlando, A Silva, S Macedo-Ribeiro, D Raimondi, W Vranken
Bioinformatics 36 (7), 2076-2081, 2020
Observation selection bias in contact prediction and its implications for structural bioinformatics
G Orlando, D Raimondi, WF Vranken
Scientific Reports 6 (1), 36679, 2016
Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis
D Raimondi, G Orlando, WF Vranken, Y Moreau
Scientific reports 9 (1), 16932, 2019
PyUUL provides an interface between biological structures and deep learning algorithms
G Orlando, D Raimondi, R Duran-Romaña, Y Moreau, J Schymkowitz, ...
Nature communications 13 (1), 961, 2022
In silico prediction of in vitro protein liquid–liquid phase separation experiments outcomes with multi-head neural attention
D Raimondi, G Orlando, E Michiels, D Pakravan, A Bratek-Skicki, ...
Bioinformatics 37 (20), 3473-3479, 2021
Ultra-fast global homology detection with discrete cosine transform and dynamic time warping
D Raimondi, G Orlando, Y Moreau, WF Vranken
Bioinformatics 34 (18), 3118-3125, 2018
SVM-dependent pairwise HMM: an application to protein pairwise alignments
G Orlando, D Raimondi, T Khan, T Lenaerts, WF Vranken
Bioinformatics 33 (24), 3902-3908, 2017
Clustering-based model of cysteine co-evolution improves disulfide bond connectivity prediction and reduces homologous sequence requirements
D Raimondi, G Orlando, WF Vranken
Bioinformatics 31 (8), 1219-1225, 2015
Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome
D Raimondi, G Orlando, F Tabaro, T Lenaerts, M Rooman, Y Moreau, ...
Scientific reports 8 (1), 16980, 2018
b2bTools: online predictions for protein biophysical features and their conservation
LP Kagami, G Orlando, D Raimondi, F Ancien, B Dixit, J Gavaldá-García, ...
Nucleic acids research 49 (W1), W52-W59, 2021
An evolutionary view on disulfide bond connectivities prediction using phylogenetic trees and a simple cysteine mutation model
D Raimondi, G Orlando, WF Vranken
PloS one 10 (7), e0131792, 2015
Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index
G Orlando, D Raimondi, W F. Vranken
Nature communications 10 (1), 2511, 2019
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